{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0d6bb72c-ffe3-4ff8-95a5-34813cc04f9d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#1.准备数据\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import os\n",
    "import time\n",
    "from IPython import display\n",
    "mnist=tf.keras.datasets.mnist\n",
    "(x_train,y_train),(_,_)=mnist.load_data()\n",
    "x_train=x_train[y_train==8.]\n",
    "for i in range(16):\n",
    "    plt.subplot(4,4,i+1)\n",
    "    t=np.random.randint(1,x_train.shape[0])\n",
    "    plt.axis(\"off\")\n",
    "    plt.imshow(x_train[t],cmap='gray')\n",
    "plt.show()\n",
    "x_train=x_train.reshape(x_train.shape[0],28,28,1).astype('float32')\n",
    "x_train=(x_train-127.5)/127.5\n",
    "BUFFER_SIZE=6000\n",
    "BATCH_SIZE=256\n",
    "train_dataset=tf.data.Dataset.from_tensor_slices(x_train).shuffle(BUFFER_SIZE).batch(BATCH_SIZE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "e19a2748-90b8-49c9-bc1c-18235d3e3bfd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_4\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_4\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12544</span>)               │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,254,400</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ leaky_re_lu_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12544</span>)               │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_9                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">12544</span>)               │          <span style=\"color: #00af00; text-decoration-color: #00af00\">50,176</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ reshape_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Reshape</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_transpose_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)           │         <span style=\"color: #00af00; text-decoration-color: #00af00\">819,200</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ leaky_re_lu_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_10               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)           │             <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_transpose_10                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │         <span style=\"color: #00af00; text-decoration-color: #00af00\">204,800</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)                    │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ leaky_re_lu_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_11               │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │             <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">BatchNormalization</span>)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_transpose_11                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">28</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)           │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,600</span> │\n",
       "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2DTranspose</span>)                    │                             │                 │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12544\u001b[0m)               │       \u001b[38;5;34m1,254,400\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ leaky_re_lu_11 (\u001b[38;5;33mLeakyReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12544\u001b[0m)               │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_9                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m12544\u001b[0m)               │          \u001b[38;5;34m50,176\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ reshape_3 (\u001b[38;5;33mReshape\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m256\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_transpose_9 (\u001b[38;5;33mConv2DTranspose\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m)           │         \u001b[38;5;34m819,200\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ leaky_re_lu_12 (\u001b[38;5;33mLeakyReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_10               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m)           │             \u001b[38;5;34m512\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_transpose_10                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │         \u001b[38;5;34m204,800\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)                    │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ leaky_re_lu_13 (\u001b[38;5;33mLeakyReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ batch_normalization_11               │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │             \u001b[38;5;34m256\u001b[0m │\n",
       "│ (\u001b[38;5;33mBatchNormalization\u001b[0m)                 │                             │                 │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_transpose_11                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m28\u001b[0m, \u001b[38;5;34m1\u001b[0m)           │           \u001b[38;5;34m1,600\u001b[0m │\n",
       "│ (\u001b[38;5;33mConv2DTranspose\u001b[0m)                    │                             │                 │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,330,944</span> (8.89 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m2,330,944\u001b[0m (8.89 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">2,305,472</span> (8.79 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m2,305,472\u001b[0m (8.79 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">25,472</span> (99.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m25,472\u001b[0m (99.50 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#2.构建生成模型\n",
    "def make_generator_model():\n",
    "    model=tf.keras.Sequential()\n",
    "    model.add(tf.keras.layers.Dense(7*7*256,use_bias=False,input_shape=(100,)))\n",
    "    model.add(tf.keras.layers.LeakyReLU())\n",
    "    model.add(tf.keras.layers.BatchNormalization())\n",
    "    model.add(tf.keras.layers.Reshape((7,7,256)))\n",
    "    model.add(tf.keras.layers.Conv2DTranspose(128,(5,5),strides=(1,1),padding='SAME',use_bias=False))\n",
    "    model.add(tf.keras.layers.LeakyReLU())\n",
    "    model.add(tf.keras.layers.BatchNormalization())\n",
    "    model.add(tf.keras.layers.Conv2DTranspose(64,(5,5),strides=(2,2),padding='SAME',use_bias=False))\n",
    "    model.add(tf.keras.layers.LeakyReLU())\n",
    "    model.add(tf.keras.layers.BatchNormalization())\n",
    "    model.add(tf.keras.layers.Conv2DTranspose(1,(5,5),strides=(2,2),padding='SAME',use_bias=False,activation='tanh'))\n",
    "    return model\n",
    "generator=make_generator_model()\n",
    "generator.summary()\n",
    "noise=tf.random.normal([1,100])\n",
    "generated_image=generator(noise,training=False)\n",
    "plt.imshow(generated_image[0,:,:,0],cmap='gray')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "fb58fa30-4cc0-45ea-94bd-bc19fca1b7f8",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\convolutional\\base_conv.py:113: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_6\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_6\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,664</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ leaky_re_lu_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">14</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)          │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>)                    │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)           │         <span style=\"color: #00af00; text-decoration-color: #00af00\">204,928</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ leaky_re_lu_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LeakyReLU</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">7</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)           │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Flatten</span>)                  │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">6272</span>)                │               <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │           <span style=\"color: #00af00; text-decoration-color: #00af00\">6,273</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ conv2d_4 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │           \u001b[38;5;34m1,664\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ leaky_re_lu_16 (\u001b[38;5;33mLeakyReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m14\u001b[0m, \u001b[38;5;34m64\u001b[0m)          │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ conv2d_5 (\u001b[38;5;33mConv2D\u001b[0m)                    │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m)           │         \u001b[38;5;34m204,928\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ leaky_re_lu_17 (\u001b[38;5;33mLeakyReLU\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m7\u001b[0m, \u001b[38;5;34m128\u001b[0m)           │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ flatten_2 (\u001b[38;5;33mFlatten\u001b[0m)                  │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m6272\u001b[0m)                │               \u001b[38;5;34m0\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │           \u001b[38;5;34m6,273\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">212,865</span> (831.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m212,865\u001b[0m (831.50 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">212,865</span> (831.50 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m212,865\u001b[0m (831.50 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "tf.Tensor([[-0.00337209]], shape=(1, 1), dtype=float32)\n"
     ]
    }
   ],
   "source": [
    "#3.构建判别模型\n",
    "def make_discriminator_model():\n",
    "    model=tf.keras.Sequential()\n",
    "    model.add(tf.keras.layers.Conv2D(64,(5,5),strides=(2,2),padding='SAME',input_shape=[28,28,1]))\n",
    "    model.add(tf.keras.layers.LeakyReLU())\n",
    "    model.add(tf.keras.layers.Dropout(0.3))\n",
    "    model.add(tf.keras.layers.Conv2D(128,(5,5),strides=(2,2),padding='SAME'))\n",
    "    model.add(tf.keras.layers.LeakyReLU())\n",
    "    model.add(tf.keras.layers.Dropout(0.3))\n",
    "    model.add(tf.keras.layers.Flatten())\n",
    "    model.add(tf.keras.layers.Dense(1))\n",
    "    return model\n",
    "discriminator=make_discriminator_model()\n",
    "discriminator.summary()\n",
    "decision=discriminator(generated_image)\n",
    "print(decision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "6a1bab29-b0f7-4bed-96ee-5a108db20513",
   "metadata": {},
   "outputs": [],
   "source": [
    "#4.定义损失函数和优化器\n",
    "cross_entropy=tf.keras.losses.BinaryCrossentropy(from_logits=True)\n",
    "def discriminator_loss(real_output,fake_output):\n",
    "    real_loss=cross_entropy(tf.ones_like(real_output),real_output)\n",
    "    fake_loss=cross_entropy(tf.zeros_like(fake_output),fake_output)\n",
    "    total_loss=real_loss+fake_loss\n",
    "    return total_loss\n",
    "def generator_loss(fake_output):\n",
    "    return cross_entropy(tf.ones_like(fake_output),fake_output)\n",
    "generator_optimizer=tf.keras.optimizers.Adam(1e-4)\n",
    "discriminator_optimizer=tf.keras.optimizers.Adam(1e-4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "bb666a1a-f7e2-4dd0-af39-7326008473d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "#5定义检查点\n",
    "checkpoint_dir='./checkpoints'\n",
    "checkpoint_prefix=os.path.join(checkpoint_dir,\"ckpt\")\n",
    "checkpoint=tf.train.Checkpoint(generator_optimizer=generator_optimizer,discriminator_optimizer=discriminator_optimizer,generator=generator,discriminator=discriminator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c1bbbac-b916-458d-bd48-97428551255b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 400x400 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "第9次训练花费12.928272724151611秒\n"
     ]
    }
   ],
   "source": [
    "#6.训练模型\n",
    "EPOCHS=500\n",
    "noise_dim=100\n",
    "num_examples_to_generate=16\n",
    "seed=tf.random.normal([num_examples_to_generate,noise_dim])\n",
    "def train_step(images):\n",
    "    noise=tf.random.normal([BATCH_SIZE,noise_dim])\n",
    "    with tf.GradientTape() as gen_tape,tf.GradientTape() as disc_tape:\n",
    "        generated_images=generator(noise,training=True)\n",
    "        real_output=discriminator(images,training=True)\n",
    "        fake_output=discriminator(generated_images,training=True)\n",
    "        gen_loss=generator_loss(fake_output)\n",
    "        disc_loss=discriminator_loss(real_output,fake_output)\n",
    "    gradients_of_generator=gen_tape.gradient(gen_loss,generator.trainable_variables)\n",
    "    gradients_of_discriminator=disc_tape.gradient(disc_loss,discriminator.trainable_variables)\n",
    "    generator_optimizer.apply_gradients(zip(gradients_of_generator,generator.trainable_variables))\n",
    "    discriminator_optimizer.apply_gradients(zip(gradients_of_discriminator,discriminator.trainable_variables))\n",
    "def generate_and_save_image(model,epoch,test_input):\n",
    "    predictions=model(test_input,training=False)\n",
    "    fig=plt.figure(figsize=(4,4))\n",
    "    for i in range(predictions.shape[0]):\n",
    "        plt.subplot(4,4,i+1)\n",
    "        plt.imshow(predictions[i,:,:,0]*127.5+127.5,cmap='gray')\n",
    "        plt.axis('off')\n",
    "    plt.show()\n",
    "    plt.savefig('image_at_epoch_{:04d}.png'.format(epoch))\n",
    "def train(dataset,epochs):\n",
    "    for epoch in range(epochs):\n",
    "        start=time.time()\n",
    "        for image_batch in dataset:\n",
    "            train_step(image_batch)\n",
    "        display.clear_output(wait=True)\n",
    "        generate_and_save_image(generator,epoch+1,seed)\n",
    "        if (epoch+1)%10==0:\n",
    "            checkpoint.save(file_prefix=checkpoint_prefix)\n",
    "        print('第{}次训练花费{}秒'.format(epoch+1,time.time()-start))\n",
    "train(train_dataset,EPOCHS)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
